Machine learning-based radiomics from multiparametric MRI for predicting aggressive pathology in clear cell renal cell carcinoma.

ABSTRACT:

Background

Clear cell renal cell carcinoma (ccRCC) exhibits significant biological heterogeneity, with aggressive forms demonstrating poor prognosis. Accurate preoperative discrimination between aggressive and indolent ccRCC is critical for individualized treatment but remains challenging. This study aimed to evaluate the performance of machine learning models based on multiparametric MRI radiomics for distinguishing aggressive from indolent ccRCC.

Methods

This retrospective study included 157 patients with pathologically confirmed ccRCC, comprising 114 indolent and 43 aggressive cases. Regions of interest (ROIs) were manually delineated on five MRI sequences: T1-weighted imaging (T1WI), T2-weighted imaging (T2WI), as well as the corticomedullary, nephrographic, and excretor

SUBMITTER: Zhan J 

PROVIDER: S-EPMC12751400 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature